A standardized framework to test event-based experiments
Bibliographic record
Abstract
The replication crisis in experimental psychology and neuroscience has received much attention recently. This has led to wide acceptance of measures to improve scientific practices, such as preregistration and registered reports. Less effort has been devoted to performing and reporting the results of systematic tests of the functioning of the experimental setup itself. Yet, inaccuracies in the performance of the experimental setup may affect the results of a study, lead to replication failures, and importantly, impede the ability to integrate results across studies. Prompted by challenges we experienced when deploying studies across six laboratories collecting electroencephalography (EEG)/magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and intracranial EEG (iEEG), here we describe a framework for both testing and reporting the performance of the experimental setup. In addition, 100 researchers were surveyed to provide a snapshot of current common practices and community standards concerning testing in published experiments' setups. Most researchers reported testing their experimental setups. Almost none, however, published the tests performed or their results. Tests were diverse, targeting different aspects of the setup. Through simulations, we clearly demonstrate how even slight inaccuracies can impact the final results. We end with a standardized, open-source, step-by-step protocol for testing (visual) event-related experiments, shared via protocols.io. The protocol aims to provide researchers with a benchmark for future replications and insights into the research quality to help improve the reproducibility of results, accelerate multicenter studies, increase robustness, and enable integration across studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.209 | 0.307 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".